论文标题
将语音说明纳入自动驾驶汽车的基于模型的强化学习
Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars
论文作者
论文摘要
本文提出了一种新的方法，该方法支持自然语言语音说明，以指导训练自动驾驶汽车时进行深度强化学习（DRL）算法。 DRL方法是自动驾驶汽车（AV）代理的流行方法。但是，大多数现有的方法都是样本和时间的，并且缺乏与人类专家的自然通信渠道。在本文中，新的人类驾驶员如何向人类教练学习，激励我们研究人类学习的新方法，并为代理商学习更自然，更平易近人的培训界面。我们建议将自然语言语音说明（NLI）纳入基于模型的深度强化学习以训练自动驾驶汽车。我们与Carla模拟器中的一些最先进的DRL方法一起评估了所提出的方法。结果表明，NLI可以帮助缓解训练过程，并大大提高代理商的学习速度。
This paper presents a novel approach that supports natural language voice instructions to guide deep reinforcement learning (DRL) algorithms when training self-driving cars. DRL methods are popular approaches for autonomous vehicle (AV) agents. However, most existing methods are sample- and time-inefficient and lack a natural communication channel with the human expert. In this paper, how new human drivers learn from human coaches motivates us to study new ways of human-in-the-loop learning and a more natural and approachable training interface for the agents. We propose incorporating natural language voice instructions (NLI) in model-based deep reinforcement learning to train self-driving cars. We evaluate the proposed method together with a few state-of-the-art DRL methods in the CARLA simulator. The results show that NLI can help ease the training process and significantly boost the agents' learning speed.
